from lda import LDA
from matplotlib.colors import ListedColormap
from sklearn.datasets import load_iris, make_classification, make_moons, make_circles
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier


def RandomForestClassifier1():
    iris = load_iris()   # 这里是sklearn中自带的一部分数据
    df = pd.DataFrame(iris.data, columns=iris.feature_names) # 格式化数据
    print (df)          #
    df['is_train'] = np.random.uniform(0, 1, len(df)) <= .75
    df['species'] = pd.Categorical.from_codes(iris.target, iris.target_names)  ## 新接口 数据
    df.head()

    train, test = df[df['is_train']==True], df[df['is_train']==False]

    features = df.columns[:4]
    clf = RandomForestClassifier(n_jobs=2)
    y, _ = pd.factorize(train['species'])
    clf.fit(train[features], y)  # 用train来训练样本

    test_pred=clf.predict(test[features])   #用测试数据来做预测
    preds = iris.target_names[test_pred]
    df2 = pd.crosstab(test['species'], preds, rownames=['actual'], colnames=['preds'])
    print (df2)
def other1():

    import numpy as np
    import matplotlib.pyplot as plt
    from matplotlib.colors import ListedColormap
    #from sklearn.cross_validation import train_test_split
    from sklearn.preprocessing import StandardScaler
    from sklearn.datasets import make_moons, make_circles, make_classification
    from sklearn.neighbors import KNeighborsClassifier
    from sklearn.svm import SVC
    from sklearn.tree import DecisionTreeClassifier
    from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
    from sklearn.naive_bayes import GaussianNB
    from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
    from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis as QDA
    # 与其他机器学习分类算法进行对比：
    h = .02  # step size in the mesh
    figure = plt.figure(figsize=(27, 9))
    names = ["Nearest Neighbors", "Linear SVM", "RBF SVM", "Decision Tree",
             "Random Forest", "AdaBoost", "Naive Bayes", "LDA", "QDA"]
    classifiers = [
        KNeighborsClassifier(3),
        SVC(kernel="linear", C=0.025),
        SVC(gamma=2, C=1),
        DecisionTreeClassifier(max_depth=5),
        RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
        AdaBoostClassifier(),
        GaussianNB(),
        LDA(),
        QDA()]

    X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,
                               random_state=1, n_clusters_per_class=1)
    rng = np.random.RandomState(2)
    X += 2 * rng.uniform(size=X.shape)
    linearly_separable = (X, y)

    datasets = [make_moons(noise=0.3, random_state=0),
                make_circles(noise=0.2, factor=0.5, random_state=1),
                linearly_separable
                ]


    i = 1
    # iterate over datasets
    for ds in datasets:
        # preprocess dataset, split into training and test part
        X, y = ds
        X = StandardScaler().fit_transform(X)
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4)

        x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
        y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
        xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                             np.arange(y_min, y_max, h))

        # just plot the dataset first
        cm = plt.cm.RdBu
        cm_bright = ListedColormap(['#FF0000', '#0000FF'])
        ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
        # Plot the training points
        ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
        # and testing points
        ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)
        ax.set_xlim(xx.min(), xx.max())
        ax.set_ylim(yy.min(), yy.max())
        ax.set_xticks(())
        ax.set_yticks(())
        i += 1

        # iterate over classifiers
        for name, clf in zip(names, classifiers):
            ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
            clf.fit(X_train, y_train)
            score = clf.score(X_test, y_test)

            # Plot the decision boundary. For that, we will assign a color to each
            # point in the mesh [x_min, m_max]x[y_min, y_max].
            if hasattr(clf, "decision_function"):
                Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
            else:
                Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]

            # Put the result into a color plot
            Z = Z.reshape(xx.shape)
            ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)

            # Plot also the training points
            ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
            # and testing points
            ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright,
                       alpha=0.6)

            ax.set_xlim(xx.min(), xx.max())
            ax.set_ylim(yy.min(), yy.max())
            ax.set_xticks(())
            ax.set_yticks(())
            ax.set_title(name)
            ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'),
                    size=15, horizontalalignment='right')
            i += 1

        figure.subplots_adjust(left=.02, right=.98)
        plt.show()

other1()